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Liquefied natural gas (LNG) regasification terminals have specification that necessitates adjustment of heating value of LNG feed. One strategy to reduce heating value is to separate heavier hydrocarbons from LNG by utilizing LNG cold energy. The amount of cold energy utilized in recovering heavy hydrocarbon is much less resulting in wastage of remaining cold energy. To reverse this wastage, we propose a polygeneration process that encompasses an organic Rankine cycle to generate electricity and refrigeration for cold storage warehouse cooling and data center cooling by utilizing remaining cold energy. Based on process simulation and optimization, we found that a significant amount of cold energy is utilized resulting in an avoided CO2 emission, thereby enhancing sustainability of an LNG regasification terminal. [ABSTRACT FROM AUTHOR]

Compression resin transfer moulding (CRTM) has been widely used to manufacture automotive parts with reduced production cycle times. With the development of fast curing thermosetting resins, the CRTM process is a viable option for the high production rates in the transportation industry. However, the dynamic resin curing behaviour poses a potential risk of manufacturing defects in the part. In order to reduce the risk during the development of the tool and the process parameters, this paper proposes a modelling framework for the CRTM process when using fast curing resin systems. The work specifically focused on the coupling between heat transfer, resin cure, resin flow and preform compaction using a commercial code, PAM-RTM. The tool captures accurately the preform filling, temperature and resin pressure evolution during the injection and compression phase. The application of the framework was demonstrated for a complex 3D demonstrator. The predicted preform filling had an accuracy of 73% for the flow front evolution compared to the experimental results. This work demonstrates the validity of the framework proposed when dealing with resin systems that are challenging to process. [ABSTRACT FROM AUTHOR]

AbstractThis work aims to develop a comprehensive kinetic and simulation study of biodiesel production using waste cooking oil (WCO) catalyzed by blast furnace slag geopolymer (BFSG) as a heterogeneous catalyst. The kinetic investigation was established following the pseudo-first and second-order model using three reaction parameters, namely, the reaction temperature (40–60 °C), reaction time (4–8 h) and catalyst ratio (6–14 wt.%), while maintaining a constant methanol-to-oil composition of 40 wt.%. The geopolymer-catalyzed transesterification process was simulated using ChemCAD version 8.1.0, which incorporates the four major triglycerides (triolein, tripalmitin, tristearin and triolein) of WCO. The results of the transesterification reaction of WCO in a kinetic plug flow reactor (PFR) demonstrated a good fit of the data, with an R2 above 0.96 in both cases. The pseudo-first-order (PFO) model revealed a more favorable reaction pathway, with an activation energy of 58.876 kJ.mol−1, as opposed to the value of 131.369 kJ.mol−1 obtained from the pseudo-second-order (PSO) analysis. The catalytic activity of BFSG yielded a maximum conversion of 99.18% at a 12 wt.% catalyst ratio. The study results demonstrated the effectiveness of the transesterification process catalyzed by BFSG as a promising low-cost technology for the biodiesel industry. [ABSTRACT FROM AUTHOR]

Carbon capture utilization and storage‐enhanced oil recovery (EOR) is often considered the most promising technology for utilizing CO2. Cryogenic distillation is also viewed as the most reasonable separation option for handling CO2‐EOR gases, despite being an energy‐intensive process. The main challenge for this technology is energy loss. To overcome this challenge, one potential alternative is to optimize the system processes and parameters. This study proposes a new process to reduce distillation energy consumption by refluxing the distillate back to the distillation column. Operational parameter optimization was performed using the commercial simulator Aspen HYSYS for modeling and sensitivity analysis of process parameters using orthogonal experimental methods. The simulation results indicate that after optimization, the energy consumption in the distillation process decreased from 0.207 to 0.196 MJ kg−1, whereas the purity decreased slightly from 94.63 % to 94.52 %. However, the recovery increased from 97.8 % to 97.88 %, and the total energy consumption decreased from 0.772 to 0.761 MJ kg−1. [ABSTRACT FROM AUTHOR]

The hydrodeoxygenation of triglycerides and fatty acids (HEFA) represents a well-established technological pathway for producing renewable drop-in fuels, such as renewable diesel, aviation fuel, or renewable light ends, like naphtha and LPG. A hydrogen supply is required, and the conventional method involves steam methane reforming (SMR) from natural gas. Both HEFA and SMR processes have been investigated in the literature separately. In this context, the goal of this work is to assess the hydrodeoxygenation process (HDO) of triglycerides/fatty acids integrated with hydrogen production. Two scenarios are compared by process simulation, equipment sizing, and economic analysis: one with traditional SMR (SC-A) and another with electrified SMR (SC–B), which is a recent alternative for hydrogen production with no natural gas. The HDO capacity was 6220 BPSD (38000 kg/h) of soybean oil, and the average yields for renewable diesel, naphtha, and LPG were 71.23 %-wt, 1.80 %-wt, and 5.87 %-wt, accordingly. Both scenarios were technical feasible and may be economically attractive. However, the grey hydrogen source was more profitable per its plant simplicity — although the CO 2 taxation may impact its profitability. • H 2 supply is need for drop-in fuels production through steam methane reforming (SMR). • Electrified and conventional SMR were compared unified with drop-in fuels production. • Both systems were technically and economically feasible. • E-SMR was more advantageous with cheap electricity and no availability of natural gas. • Both the processes have similar carbon footprints from the process perspective. [ABSTRACT FROM AUTHOR]

Software process simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. We aim to provide evidence‐based support of the process metrics for software process (simulation) modeling. A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following our proposed ontology of metrics in SPS. We identify 131 process modeling studies that collectively involve 1975 raw metrics and classified them into 21 categories using the coding technique. We found product and process external metrics are not used frequently in SPS modeling while resource external metrics are widely used. We analyze the causal relationships between metrics. We find that the models exhibit significant diversity, as no pairwise relationship between metrics accounts for more than 10% SPS models. We identify 17 data issues may encounter in measurement and 10 coping strategies. The results of this study provide process modelers with an evidence‐based reference of the identification and the use of metrics in SPS modeling and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice. [ABSTRACT FROM AUTHOR]

Among metal additive manufacturing technologies, direct energy deposition (DED) processes have the advantage to be easily integrable in a manufacturing chain with other conventional technologies. This characteristic can be exploited by designing reinforcement structures to be added by DED onto pre-existing subcomponents to tailor the part's mechanical properties while keeping the part lightweight. This study focuses on DED by means of laser beam and powder process optimization to improve material quality and geometrical accuracy of AlSi10Mg reinforcement structures while preventing excessive thermal deformations and material dilution into the substrate. These results are compared with finite elements numerical simulations of the deposition process, comprising thermo-elastic deformation and material deposition, to predict the bending and reinforcement of the processed substrate. In particular, the model includes the deterministic prediction of the deposition profile as a function of the process parameters and a few condition-specific coefficients: once calibrated, the model was used to compare the numerical and experimental residual deformation of the reinforced sample, obtaining promising agreement. The reinforcement provided to a 1.5 mm thick substrate by a single wall of deposited materials, with cross-sectional dimensions of 2 mm in width and 2.5 mm in height, was evaluated by three points bending. With the reinforcement on the tensile side of the stresses, the energy absorbed by the material plastic deformation increased by 2.4% as compared to the substrate alone, while with the reinforcement on the compression side of the stresses the energy absorption increased by 75.8% on average. [ABSTRACT FROM AUTHOR]

Invar 36 alloy has an extremely low coefficient of thermal expansion and is suitable for preparing a fine metal mask (FMM) for the evaporation of silicon-based organic light emitting display microdisplays. The higher the resolution and the greater the number of pixels, the finer and more delicate the holes required. Femtosecond laser processing technology is an effective technical means for processing Invar 36 alloy FMM due to its characteristics of extremely short pulse width, extremely high pulse energy density, smaller heat-affected zone, and more regular machining edges. The femtosecond laser processing parameters directly affect temperature distribution and then affect the manufacturing quality and efficiency of Invar 36 alloy FMM. At present, femtosecond laser processing of Invar 36 alloy FMM is still in the exploration stage, and the processing mechanism and technology are not clear. In this paper, a simulation model of single-pulse femtosecond laser processing Invar 36 alloy is established based on the two-temperature equation. By comparing the ablation morphology of the simulation output with the experimental measurement results, the validity of the simulation model is verified and the processing mechanism is preliminarily explored. Based on the simulation model, the effects of femtosecond laser energy density, pulse duration, and spot diameter on the electron temperature field, lattice temperature field, and ablation morphology of the craters are explored. The process window of laser processing parameters is determined. A full-factor test is conducted. Take the minimum length of the heat-affected zone, the maximum depth of the ablation crater, and the minimum diameter of the ablation crater as three optimization objectives and obtain Pareto optimal solutions based on genetic algorithm. The optimization of single-pulse femtosecond laser drilling Invar 36 alloy is realized. [ABSTRACT FROM AUTHOR]

Edible mushrooms are gaining popularity as functional foods, not only for their distinctive taste and subtle flavor but also for their potential health benefits. They are rich in essential nutrients, including carbohydrates, proteins, fiber, minerals, and vitamins. However, it is estimated that up to 30% of total mushroom production generates non-marketable waste, which is currently used as animal feed, fertilizer, or compost. From a circular economy perspective, the objective of this work is to propose a conceptual design for a biorefinery to produce high-value-added products from non-marketable edible mushroom waste and to conduct a comprehensive assessment using modeling and simulation tools for process engineering. The general approach first involves identifying high-potential products, then determining the best processing routes, followed by developing a process flowsheet diagram for a case study (A. bisporus), and ultimately conducting a comprehensive assessment of the biorefinery to evaluate its sustainability. As a result, the proposed biorefinery may produce chitin, citric acid, and fertilizer, demonstrating the technical feasibility of waste valorization in terms of product yields, with positive profitability in terms of the rate of return on investment and payback period, and low environmental impact in terms of water and energy consumption as well as CO2 emissions. [ABSTRACT FROM AUTHOR]

Process simulation was integrated with life cycle assessment to evaluate process efficiency and environmental impact of the production of manganese ferroalloys for various production modes and different ore mineralogies. Utilizing HSC Sim, the model was designed to evaluate the production process with or without a pretreatment step, where results for simulated cases using a four-zone ferromanganese furnace model were exported to openLCA for a complete life cycle assessment. Two main production scenarios were simulated: a closed ferromanganese furnace running on the duplex method and an open furnace running on the discard slag approach. The closed furnace scenario achieved a 17.5% reduction in energy consumption and a 16.2% decrease in direct CO2 emissions with CO-rich off-gas pretreatment. The open furnace scenario showed an 11.2% reduction in energy consumption with thermal solar energy pretreatment but no change in CO2 emissions. [ABSTRACT FROM AUTHOR]

The process-induced deformation (PID) during the manufacturing of thermosetting composite materials can significantly compromise manufacturing precision. This paper introduces an innovative method that combines a finite element analysis (FEA), feature classification algorithms, and an Artificial Neural Network (ANN) framework to rapidly predict the PID of a typical L-shaped structure. Initially, a comprehensive range of parameters that influence PID are compiled in this research, followed by the generation of a dataset through FEA considering viscoelastic constitutive models, validated by experimental results. Influential parameters are classified using Random Forest and LASSO regression methods, with each parameter rated according to its impact on PID, delineating their varying degrees of importance. Subsequently, through a hyperparameter analysis, an ANN framework is developed to rapidly predict the PID, while also refining the assessment of the parameters' significance. This innovative approach achieves a computational time reduction of 98% with less than a 5% loss in accuracy, and highlights that under limited computational conditions, considering only a subset or all of the parameters—the peak temperature, corner angle, coefficient of chemical shrinkage, coefficient of thermal expansion, curing pressure, and E 1 —minimizes accuracy loss. The study demonstrates that machine learning algorithms can effectively address the challenge of predicting composite material PID, providing valuable insights for practical manufacturing applications. [ABSTRACT FROM AUTHOR]

The excellent load-bearing performance of sized warp yarns can reduce the end-breakage rate and enable them to complete weaving smoothly. Evaluating the load-bearing performance of sized warp yarn can guide the sizing process and weaving production. For this purpose, the study evaluated the load-bearing performance of cotton sized warp yarn. First, based on the JN-01 tester, a weaving load simulation tester of sized warp yarn, the test sample capacity for the load-bearing life was determined to be 50 ends using a U-test. Second, by testing three cotton sized warp yarns with different linear densities, the universality of the parameter conditions of the tester was verified. Third, with the JN-01 tester, the breaking strength and hairiness under different load-bearing cycles were measured, and it was found that the breaking strength of the sized yarn was linearly related to the load-bearing cycles. In contrast, the hairiness of the sized yarn was logarithmically related to the load-bearing cycles. According to regression analyses, evaluation indicators were extracted to characterize the strength loss resistance and anti-fuzzing performance of sized warp yarns, which were the residual enhancement rate and hairiness after friction. The evaluation results of sized warp yarns, with different linear densities and different sizing ratios, showed the effectiveness of the evaluation indicators. Finally, by verifying the effectiveness of samples from three factories, based on the evaluation indicators in this paper, the results revealed that the constructed evaluation method effectively evaluated the load-bearing performance of cotton sized warp yarn. [ABSTRACT FROM AUTHOR]

In cassava ethanol studies, few studies have combined production data from plants with simulation models, and further investigated the environmental emissions caused by different production processes. In this paper, based on the survey data of cassava ethanol plants in Guangxi, we established an ethanol production process model and conducted a life cycle assessment of the raw meal fermentation (RMF) process and traditional clinker fermentation (CF) process. The impact of fermentation broth alcohol concentration on the environmental emissions of the whole process was also investigated based on the conventional CF process. The results showed that the RMF production process had more advantages than the CF production process in terms of energy savings, with 633 MJ per ton of ethanol and 37.39 kg eq/t ethanol of CO2 reduction. Increasing the alcohol concentration of the fermentation broth facilitated the removal of environmental emissions from the process. All cassava ethanol production models exhibited net energy ratios of no less than 2.74. Of these, the RMF 15% showed the most competitive net energy ratio of 2.97, the highest renewability of 4.52, and the lowest environmental emissions. A detailed analysis of the environmental impacts by the ethanol production phase showed that the distillation section was the critical point for energy saving and emission reduction. Sensitivity analysis showed that fertilizer and natural gas consumption could not be ignored. [ABSTRACT FROM AUTHOR]

Recycling hydrofluoric acid (HF) is a significant issue within the field of organofluoride chemistry. A key obstacle in this endeavor lies in the separation of azeotropic mixtures containing HF, such as the azeotropic mixture of HF and 1,1,1,3,3-pentafluoropropane (HFC-245fa), which exhibit similar boiling points. This study investigates the application of extractant distillation (ED) with ionic liquids (ILs) to achieve the separation of HF/HFC-245fa at the molecular scale, cell dimension, and systematic level (including the optimization and analysis of ED). COSMO-RS was employed to identify the suitable IL, with [Tf2N]-based ILs emerging as the most optimal extractants. Four representative [Tf2N]-based ILs were chosen based on their physical properties and thermodynamic behavior, and were applied in a continuous ED process simulation for the separation of HF/HFC-245fa using Aspen Plus, resulting in the production of 99.5 mol% HFC-245fa and 99.5 mol% HF. The determination of the optimal operational parameters was carried out through sequential quadratic programming, considering the purity requirements. An assessment of energy consumption analysis indicated that [C1Py][Tf2N] stands out as the most suitable IL for separation of HF/HFC-245fa, with heat and cooling duties of 467.82 kW and 304.80 kW, respectively. Economic analysis for the process with [C1Py][Tf2N] indicated that the annual operating costs and equipment costs are $ 5.58 × 104 and $ 3.75 × 106, respectively, of which the costs of IL are 93%, the total annual cost was comparable to that of the pressure-swing distillation process, suggesting that this ED process utilizing ILs for separating azeotropes is economically viable in terms of industrial application. [ABSTRACT FROM AUTHOR]

AbstractThis study explores controlling a first- and second-generation alcoholic fermentation process with cell recycling, modeled through algebraic differential equations (DAE) with constraints. It evaluates seven controller types: PI, PID, Model Predictive Control (MPC), Neural Network Model Predictive Control (NNMPC), Mixed Model Predictive Control (MMPC), Internal Model Control (IMC), and Linear Quadratic Regulator (LQR). Results show that, while PI and PID controllers can track setpoints, they exhibit slow responses and oscillations. In contrast, MPC controllers respond faster, with both MPC and MMPC demonstrating more robust dynamics, achieving a significant reduction in Integral of the Absolute Error (IAE) across various disturbances, notably an 87% reduction in regulatory scenarios. NNMPC outperforms PI and PID but exhibits overshoot and oscillations, and lacks robustness for servo-type problems. However, MMPC showcases comparable or superior performance to MPC, surpassing other controllers in robustness, especially the IMC and LQR in the regulatory problems. NNMPC maintains a simulation time of under 4 seconds, whereas MPC incurs a computational cost 1,000 times higher. Integrating NNMPC’s optimal increment as an initial estimate in MPC reduces computational time by up to 79.7%. These findings highlight the ANN’s effectiveness in addressing complex control challenges, especially when integrated with MPC. MMPC offers a superior balance between accuracy, robustness, and computational efficiency, serving as a promising solution for reducing computational costs in MPC-type controllers. [ABSTRACT FROM AUTHOR]

Methanol is considered a key and platform compound to produce high value‐added chemicals, and it could contribute to energy transition, decreasing the dependence of fossil fuels. This paper presents a methanol synthesis simulation over a commercial Cu/ZnO/Al2O3, which is used at industrial scale. With the aim of exploring renewable raw materials as alternative to synthesize this alcohol, synthesis gas was obtained as a starting mixture from gasification of residual coffee pulp, which is an agro‐industrial waste widely generated in coffee producing countries. Currently, the raw materials most used to obtain methanol are natural gas and coal, so the use of agro‐industrial waste as a raw material would achieve greener methanol production. Design of experiments and ANOVA were applied to obtain results with statistical significance. Residual coffee pulp gasification conditions and reaction temperature were the controlled variables, while methanol production was selected as a response one. The simulation runs and ANOVA were analyzed and compared with the literature reports, and it was observed that methanol production is possible avoiding the catalyst deactivation. For the best obtained conditions, the profiles along the reactor of chemical species molar fraction, stochiometric number, methanol production and pressure drop were shown. [ABSTRACT FROM AUTHOR]

Due to growing interest in providing and storing sufficient renewable energies, energy generation from biomass is becoming increasingly important. Biomass gasification represents the process of converting biomass into hydrogen-rich syngas. A one-dimensional kinetic reactor model was developed to simulate biomass gasification processes as an alternative to cost-intensive experiments. The presented model stands out as it contains the additional value of universal use with different biomass types and a more comprehensive application due to its integration into the DWSIM process simulator. The model consists of mass and energy balances based on the kinetics of selected reactions. Two different reactor schemes are simulated: (1) a fixed bed reactor and (2) a fluidized bed reactor. The operating mode can be set as isothermal or non-isothermal. The model was programmed using Python and integrated into DWSIM. Depending on incoming mass flows (biomass, oxygen, steam), biomass type, reactor type, reactor dimensions, temperature, and pressure, the model predicts the mass flows of char, tar, hydrogen, carbon monoxide, carbon dioxide, methane, and water. Comparison with experimental data from the literature validates the results gained from our model. [ABSTRACT FROM AUTHOR]

Welding current is an essential parameter for submerged arc welding process. In submerged arc welding, the enormous heat generated by the current promotes the decomposition of the oxides in the flux, releasing oxygen and increasing the oxygen level in the metal, which further affects the microstructure and mechanical properties of the weld joint. Although previous studies have developed various models to evaluate oxygen content, the thermodynamic mechanism by which current influences oxygen levels in metal remains inadequately understood. This study integrates CALPHAD technology with welding thermodynamics to predict and simulate the impact of the welding current on oxygen content in metals. By combining experimental data with thermodynamic modeling, the research investigates how different current settings affect oxygen content in the metal across various welding zones, specifically when using CaO-Al2O3 fluxes with low and high basicity indices for the welding of typical carbon steel. This study selected two current values, 300 A and 600 A, for modeling analyses of the welding process, along with two typical fluxes with basicity indices of 1.6 and 0.4. The results indicate that the proposed method outperforms the BI model and can predict the metallurgical effects of current on oxygen content in the droplet and molten pool zones. The thermodynamic mechanisms that govern the metal oxygen level are also evaluated. These findings aim to enhance the understanding of the thermodynamic mechanism that governs oxygen behavior under different current conditions, thereby contributing to the optimization of submerged arc welding process. [ABSTRACT FROM AUTHOR]

The demand for cement has significantly increased, growing by 8% in the year 2022 and by a further 12% in 2023. It is highly anticipated that this trend will continue, and it will result in significant growth by 2030. However, cement production is highly energy-intensive, with 70 to 80% of the total energy consumed during the clinker formation, which is the main cement production process. Minimising energy losses requires a radical approach that includes optimising the performance of the kilns and significantly improving their energy efficiency. One of the most efficient approaches to optimise the performance of the kilns and reduce energy losses is by integrating process re-engineering models, which leverage process data analytics, machine learning, and computational methods. This study employed a model-based integration approach to improve energy efficiency during clinker formation. Energy consumption data were collected from two semi-automated cement production plants. The data were analysed using a regression model in Minitab (Minitab 21.1.0) statistical software. The analysis resulted in a linear energy consumption equation that links energy consumption to both production and energy loss. Dynamic simulations and modelling using Simulink in MATLAB were performed based on a proportional–integral–derivative (PID)-controlled system. The dynamic behaviour of the model was evaluated using data from Plant A and validated with data from Plant B. The energy efficiency equation was established as a mathematical model that explains energy improvements based on incorporating parameters for the cement kiln system and disturbances from the environment. [ABSTRACT FROM AUTHOR]

The processing of long glass fibre-reinforced thermoplastics results in considerable fibre damage, particularly during plasticising. By using thermoplastic foam injection moulding (FIM) with a constant blowing agent atmosphere, fibre damage during plasticisation can be reduced. This can be attributed to the reduction of the melt viscosity on the one hand and the influence of the melting behaviour on the other. Therefore, the influence of the FIM and the process parameters on the fibre length and the fibre length distribution are analysed and compared with the influence of the process parameters on the melt viscosity. [ABSTRACT FROM AUTHOR]

Since the advanced Integrated Circuit (IC) manufacturing process has reached the 14 nm technology node and beyond, the pattern size of integrated circuits has become much smaller than the theoretical minimum resolution of lithography. In order to further extend the performance of immersion lithography, the industry introduces Multiple Patterning Technology (MPT) and Negative Tone Development (NTD) lithography technology. MPT and NTD lithography have greatly improved the process window of immersion lithography, enabling ArF immersion lithography to extend to 7 nm or even 5 nm technology nodes and below. However, MPT still has limited ability to improve the process window for some special patterns. For example，for the poly gate process in Front End of Line (FEOL) or the metal routing process in Back End of Line (BEOL), IC design often uses a special kind of patterns, such as the bi-line or tri-line structures. Due to the fact that some type of the bi-line/tri-line structures have a semi-dense pattern on each side, the process window of these patterns are relatively narrow, making them difficult to manufacture. This paper focuses on the patterning technology scheme of bi-line/tri-line structures in IC design under the diffraction limit of 193 nm immersion lithography. This paper explores the solution for bi-line/tri-line patterns through mask split strategy and NTD lithography process simulation with a physical model, including illumination conditions, Optical Proximity Correction (OPC) methods, and photoresist process parameter design. This study provides a reference scheme for subsequent NTD process development and patterning technology, as well as a reference idea for IC design methods.

Due to the tool-less fabrication of parts and the high degree of geometric design freedom, additive manufacturing is experiencing increasing relevance for various industrial applications. In particular, the powder bed fusion of metals using a laser beam (PBF-LB/M) process allows for the metal-based manufacturing of complex parts with high mechanical properties. However, residual stresses form during PBF-LB/M due to high thermal gradients and a non-uniform cooling. These lead to a distortion of the parts, which reduces the dimensional accuracy and increases the amount of post-processing necessary to meet the defined requirements. To predict the resulting residual stress state and distortion prior to the actual PBF-LB/M process, this paper presents the finite-element-based simulation tool AscentAM with its core module and several sub-modules. The tool is based on open-source programs and utilizes a sequentially coupled thermo-mechanical simulation, in which the significant influences of the manufacturing process are considered by their physical relations. The simulation entirely emulates the PBF-LB/M process chain including the heat treatment. In addition, algorithms for the part pre-deformation and the export of a machine-specific file format were implemented. The simulation results were verified, and an experimental validation was performed for two benchmark geometries with regard to their distortion. The application of the optimization sub-module significantly minimized the form deviation from the nominal geometry. A high level of accuracy was observed for the prediction of the distortion at different manufacturing states. The process simulation provides an important contribution to the first-time-right manufacturing of parts fabricated by the PBF-LB/M process.

Sliver nanowires (Ag NWs) are promising building blocks for electronic nanodevices, and therefore, their precise movement and assembly are urgently needed. Here, molecular dynamics simulations are conducted to model the manipulation of Ag NWs on a silicon substrate using a tungsten (W) probe. The focus is on understanding the deformation and motion mechanisms of the Ag NWs induced by the moving probe. Throughout the manipulation process, Ag NWs undergo four distinct stages: plastic deformation, deformation extension, a hybrid stage, and either movement or fracture. At the contact area between the probe and the Ag NWs, as well as in regions of stress concentration, Ag atoms are observed to transition from FCC to HCP, BCC, and even amorphous states. Notably, the BCC phase serves as an intermediate transition state during this lattice transformation. The manipulation parameters, particularly the velocity and displacement of the probe, significantly influence the outcome of the Ag NWs. High-speed manipulation with small displacements tends to result in Ag NW fracture, whereas low-speed manipulation with larger displacements facilitates the bending or movement of the Ag NWs. Drawing from the observed deformation behaviors, a nanomanipulation platform integrated within a scanning electron microscope (SEM) was developed. This platform enables the in situ pushing, cutting, bending, and rotating of Ag NWs using a W probe, employing various probe feeding strategies. By combination of two or more of these manipulation operations, complex close-loop structures, such as triangles and hexagons, were successfully assembled via Ag NWs. This research offers valuable insights into the precise and controlled manipulation of Ag NWs, paving the way for the fabrication of sophisticated, high-performance nanodevices. [ABSTRACT FROM AUTHOR]

This study presents an innovative approach utilising molecular dynamics simulations to elucidate the polymerisation and neck formation mechanisms of phenolic resin during laser powder bed fusion (L-PBF). By developing system and dual-particle models and employing infrared thermal imaging, we established a heat source model for phenolic resin under varying parameters. Our molecular dynamics simulations, using the ReaxFF reactive force field, indicate that high-power lasers significantly enhance cross-linking, achieving a peak polymerisation degree of 78.9% at 30W and 35W power levels. Conversely, increased scanning speed slows product degradation, reducing the polymerisation degree. The interplay of laser power and scanning speed influences polymerisation, with total input energy and heating rate impacting polymerisation and molecular size. Additionally, we investigated neck formation, revealing that small vortices evolve into larger ones during sintering, promoting neck formation. Optical microscopy confirmed diverse neck shapes under different conditions. Quantitative data showed that at a scanning speed of 1000 mm/s, the polymerisation degree reached 93%, with only 2 molecules remaining post-simulation. At 30W power, the largest individual molecule observed was C849H703O117. This research offers crucial insights for selective laser sintering processes, emphasising the importance of high energy input and optimal heating rates for well-formed necks. [ABSTRACT FROM AUTHOR]

During the oxidation of SiGe regions, Si is preferably incorporated into the oxide, while Ge atoms accumulate at the SiGe side of the interface. Moreover, during oxidation of fin structures of Si/SiGe superlattices, Ge atoms diffuse from SiGe regions to Si regions along the oxide/SiGe interface, as recently reported. This surface diffusion can be used for the formation of Si nanowires surrounded by SiGe, and possibly for the fabrication of gate all‐around transistors. Herein, a new process simulation model is presented which describes SiGe oxidation and the diffusion of Ge atoms along the interface. During oxidation, Ge atoms can be trapped at the oxide/SiGe interface, diffuse along the interface, and be re‐emitted into the SiGe bulk. The model reproduces measured oxidation rates, the pileup of Ge atoms at the SiGe side of planar oxide/SiGe interfaces, the injection of self‐interstitials and the reduction of vacancies at oxidizing SiGe surfaces, and the recently reported diffusion of Ge atoms along the surface of fin structures made of Si/SiGe superlattices. [ABSTRACT FROM AUTHOR]

A comprehensive process model for 4H‐SiC oxidation is created and calibrated against a very large collection of experimental data. The model reproduces measured oxide thickness for Si‐face, C‐face, and a‐face SiC wafers, in the temperature range 950–1500 °C, in the pressure range 0.25–4.0 atm, in the thickness range 3–1600 nm, and for SiC doping ranging between 1019 cm−3 n‐type and 1019 cm−3 p‐type. The model is based on the Massoud model: Oxidation is driven by oxidants (O2, H2O) which are present in the gas phase, diffuse through the oxide, and form SiO2 at the oxide–SiC interface. For thin oxides, the interface reaction rate includes empirical correction terms which add to the oxidation rate, and which asymptotically approach zero with increasing oxide thickness. For dry oxidation, a remarkable dependence on the O2 partial pressure is discovered: For thick oxides, the oxidation rate scales linearly with the pressure, but the correction term for thin oxides scales with the square root of the pressure. This suggests that the atomistic processes responsible for the fast initial growth of oxides involve the splitting of O2 molecules into two O atoms. [ABSTRACT FROM AUTHOR]

The system integration of cross‐industrial networks in the Carbon2Chem® project relies on numerical simulations. Hence, this study validates the methanol synthesis loop models vs. the measurements from a demonstration plant, focusing on the thermodynamic, kinetic, and dynamic aspects. The plant can produce up to 500 L per week of methanol from real blast furnace gases of the thyssenkrupp steel plant in Duisburg. Despite its small size, it comprises a recycle gas loop and an original reactor tube of 6 m in length. After validation, the simulation models are used to analyze the dynamic operation limits of the plant, and they are transferred to the model of an industrial size to analyze the operation of cross‐industrial networks with volatile boundary conditions. [ABSTRACT FROM AUTHOR]

Featured Application: Polystyrene recycling. This work proposes a conceptual design for recovering polystyrene (PS) using solvents of agro-industrial origin. The literature describes the dilution of expanded polystyrene (EPS) in limonene, followed by its insolubilization with alcohols for recovery. However, there is no information on the solubility limit for the PS + limonene + alcohol system, which is critical for the process design. To determine the solubility limit, we diluted the waste EPS in D-limonene, mixed it with ethanol to form a precipitate, and used a gravimetric method to measure the mass of the compounds. These results allowed for the conceptual design of an EPS recycling process using a chemical process simulator, which includes a separator, a distillation column, and auxiliary equipment such as heaters, coolers, and pumps. An empirical correlation was obtained for the solubility limit, which enabled the design of a process for the treatment of 52 kg/s of PS using 0.75 kg/s of ethanol and 2.4 kg/s of D-limonene once the stationary state had been reached. The distillation column is six-stage, with a reflux ratio of 1.5 and duties of 30,000 and −25 847 kW for the condenser and reboiler, respectively. [ABSTRACT FROM AUTHOR]

Due to the tool-less fabrication of parts and the high degree of geometric design freedom, additive manufacturing is experiencing increasing relevance for various industrial applications. In particular, the powder bed fusion of metals using a laser beam (PBF-LB/M) process allows for the metal-based manufacturing of complex parts with high mechanical properties. However, residual stresses form during PBF-LB/M due to high thermal gradients and a non-uniform cooling. These lead to a distortion of the parts, which reduces the dimensional accuracy and increases the amount of post-processing necessary to meet the defined requirements. To predict the resulting residual stress state and distortion prior to the actual PBF-LB/M process, this paper presents the finite-element-based simulation tool AscentAM with its core module and several sub-modules. The tool is based on open-source programs and utilizes a sequentially coupled thermo-mechanical simulation, in which the significant influences of the manufacturing process are considered by their physical relations. The simulation entirely emulates the PBF-LB/M process chain including the heat treatment. In addition, algorithms for the part pre-deformation and the export of a machine-specific file format were implemented. The simulation results were verified, and an experimental validation was performed for two benchmark geometries with regard to their distortion. The application of the optimization sub-module significantly minimized the form deviation from the nominal geometry. A high level of accuracy was observed for the prediction of the distortion at different manufacturing states. The process simulation provides an important contribution to the first-time-right manufacturing of parts fabricated by the PBF-LB/M process. [ABSTRACT FROM AUTHOR]

Most commercial software used to simulate the injection molding process of semi-crystalline thermoplastic polymers do not explicitly take into account the polymer crystallization, which could lead to errors in the estimations of filling as well as warpage and shrinkage. This is mainly due to the common complexity of the models used to describe crystallization and the challenging respective model parameter identification under injection molding conditions. To close this gap, in this work, we use a simple thermo-mechanical crystallization model to describe the flow-induced and quiescent crystallization of an unreinforced semi-crystalline thermoplastic material during injection molding. The crystallization model is implemented in the commercial software Autodesk® Moldflow® Insight 2021 using the Solver API feature alongside crystallization-dependent viscosity, PVT, and solidification models. The model parameters were identified using a calibration workflow that employs surrogate models representing the simulated pressure results to perform a multi-objective optimization. The filling predictions as well as the calculated pressure fields are presented using the calibrated model parameters in comparison to those measured during the actual injection molding of a polyoxymethylene (POM) part using different process conditions. The results show major improvements in the estimations of the time-depending pressure field as well as the level of filling of the produced parts. [ABSTRACT FROM AUTHOR]

This study reports the comparative energy and exergy analysis of ortho-para hydrogen and non-ortho-para hydrogen conversion in hydrogen liquefaction process. Two cases were simulated, case A – hydrogen liquefaction with ortho-parahydrogen conversion and case B – hydrogen liquefaction without ortho-parahydrogen conversion. This is the first study that presents a comparative energy and exergy analysis between such two cases. In this research, a hydrogen liquefaction process was designed adopting cascaded five-stage Brayton refrigeration cycle. The process was simulated in Aspen PLUS. The process used a mixed refrigerant (of liquefied natural gas) refrigeration cycle to precool the gaseous hydrogen feed from 26 °C temperature to −192 °C temperature, and mixed refrigerant (of nelium) was subsequently used to further deep-cool the the hydrogen stream from −192 °C temperature to −245.99 °C temperature in the cryogenic section of the process. Liquefaction was achieved by expanding the hydrogen through Joule-Thomson valve at −248.37 °C and 1 bar. The simulated results of the two cases showed the specific energy consumption of case A to be 8.45 kWhr/kg LH , and that of case B to be 15.65 kWhr/kg LH respectively. The results also indicated a total exergy efficiency of 92.42% in case A and 87.18% in case B. The research results showed that the hydrogen liquefaction designed with configuration of ortho-parahydrogen conversion has better performance indicators than the liquefaction without ortho-parahydrogen conversion. Therefore, hydrogen liquefaction with ortho-parahydrogen conversion can be considered in the design and development of new hydrogen liquefaction plants. Process optimization is recommended to further enhance the specific energy consumption and exergy efficiency of both processes. • Comparative analysis of ortho-para and non-ortho-para hydrogen conversion. • Ortho-para Process is more energy efficient than non-ortho-para process. • Average exergy efficiency of 89.8% achieved in the analysed processes. • Integration of three refrigeration cycles to achieve hydrogen liquefaction. [ABSTRACT FROM AUTHOR]

Chemical looping dry reforming of methane (CLDRM) using perovskites as a catalyst is considered a promising option for producing hydrogen from biogas. In this work, the life-cycle performance of a system compiling a CLDRM unit paired with a water gas shift unit, a pressure swing adsorption unit, and a combined cycle scheme to provide steam and electricity was assessed. The main data needed to reflect the behavior of the reforming reaction was obtained experimentally and implemented in an Aspen Plus® simulation. Inventory data was obtained through process simulation and used to assess the environmental performance of the process in terms of carbon footprint, acidification, freshwater eutrophication, ozone depletion, photochemical ozone formation, and depletion of minerals and metals. Overall, the environmental viability of the production of green hydrogen from biogas was found to be heavily dependent on the biogas leakage in anaerobic digestion plants. The CLDRM system was benchmarked against a conventional DRM implementation for the same feedstock. While the conventional DRM plant environmentally outperformed the perovskite-based CLDRM, the latter might present advantages from an implementation point of view. [Display omitted] • Scaled-up chemical looping dry reforming of biogas for hydrogen production was simulated. • The life-cycle performance of the configuration with a perovskite catalyst was assessed. • Environmental hotspots refer to biogas leakage, natural gas processes, and catalyst metals. • Environmental advantages of conventional dry reforming were linked to heat integration. • Green hydrogen qualification requires avoiding leakages from anaerobic digestion plants. [ABSTRACT FROM AUTHOR]

A numerical model of packing applied to rigid objects is presented. It aims at describing a random stack of polymer composite chips in order to model the packing step of an existing recycling technique. The geometric properties of the stack play a major role in the mechanical properties of the recycled products. Short, simple and effective geometric descriptors of the stack are proposed. Their ability to differentiate random stacks is illustrated with an example. Then, a validation is proposed based on experimental data obtained from a bench specially designed for this work. The tests consist in the free fall of square chips. Finally, the developed model is compared to other models (free fall and packing of fibers) in order to enforce its relevance in the simulation of packing of rigid objects. [ABSTRACT FROM AUTHOR]

This research introduces multi‐objective stochastic optimization (MOSO) and non‐dominated sorting differential evolution (NSDE) in‐house coded algorithms for enhancing the optimization of the resin transfer molding (RTM) process during the mold‐fill phase for composite parts. Glass fiber‐vinyl ester‐based automotive bonnet and carbon fiber‐RTM6‐based aircraft wing flap parts were used as the case studies. Initially, the NSDE algorithm was developed for the simultaneous optimization of dry‐spot content and mold‐fill time by changing the locations of gates and vents with pre‐fixed port numbers. Then, the MOSO algorithm was designed for the concurrent optimization of the dry‐spot content, mold‐fill time and total number of ports by changing both the numbers and locations of gates and vents. The effect of race‐tracking was also investigated using higher permeability values at the composite part‐cut edges. When compared, the MOSO algorithm predicted less dry‐spot content, number of ports, mold‐fill time and uniform resin flow‐front progressions with lesser functional evaluations and computational time than the NSDE algorithm. Highlights: The non‐dominated sorting differential evolution (NSDE) and multi‐objective stochastic optimization (MOSO) algorithms were developed for the optimization of the mold fill phase of resin transfer molded composite parts.The development of the NSDE algorithm aimed to concurrently optimize the dry‐spot content and mold‐fill time by changing the locations of gates and vents with pre‐fixed port numbers.The MOSO algorithm was designed to concurrently optimize dry‐spot content, mold‐fill time, and the total number of ports by varying both the numbers and locations of gates and vents.The MOSO algorithm predicted better‐automated mold‐fill phase optimalities for the resin transfer molded composite parts compared to the NSDE algorithm. [ABSTRACT FROM AUTHOR]

Objective Being a kind of traditional fossil fuel, petroleum has complex compositions, which makes it difficult to accurately control in refineries. Under the requirements of intelligent manufacturing, establishing a reasonable and reliable refining process model and accurately predicting the impact from upstream and downstream during the refining process can help achieve intelligent regulation and optimization of the refining process. Methods Based on Aspen HYSYS software and the reactor module integrated with lumped kinetic model, a rigorous simulation process of atmospheric and vacuum distillation-hydrocracking from crude oil to product was established. Based on the simulation, the influence of the volume ratio of hydrogen-containing gas to oil on the reaction products flow rate and the amount of hydrogen was investigated. On this basis, the change of product output of downstream hydrocracking unit was obtained by using Matlab to drive process simulation under the condition of fluctuation of upstream crude oil ratio. Results The simulation had high accuracy, with an average relative error of 0.41%. When the proportion of domestic crude oil increased from 20% to 50%, the production of diesel products decreased by 13.9%, the production of naphtha products increased by 6.1%, and total production decreased by 8.7%. Conclusions Based on the program-driven Aspen HYSYS, an atmospheric and vacuum distillation and hydrocracking model have been established, which includes the complete process from petroleum to products, and can analyze and optimize refining process. This model contributes to the implementation of intelligent refining and the transformation of traditional refining enterprises towards intelligence and digitization. [ABSTRACT FROM AUTHOR]

Recent SARS-CoV-2 pandemic elevated research interest in microorganism-related diseases, and protective health application importance such as vaccination and immune promoter agents emerged. Among the production methods for proteins, recombinant technology is an efficient alternative and frequently preferred method. However, since the production and purification processes vary due to the protein nature, the effect of these differences on the cost remains ambiguous. In this study, brucellosis and its two important vaccine candidate proteins (rOmp25 and rEipB) with different properties were selected as models, and industrial-scale production processes were compared with the SuperPro Designer® for estimating the unit production cost. Simulation study showed raw material cost by roughly 60% was one of the barriers to lower-cost production and 52.5 and 559.8 $/g were estimated for rEipB and rOmp25, respectively. Techno-economic evaluation of recombinant protein produced for vaccine purposes Recombinant proteins rOmp25 and rEipB production process using E.coli BL21 Effect of outer membrane and periplasmic space proteins on purification cost Simulated cost estimation of rEipB and rOmp25 were 52.5 and 559.8 $/g, respectively [ABSTRACT FROM AUTHOR]

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits. [ABSTRACT FROM AUTHOR]

This study examines a numerical method to simulate the production of novel multi-material metal-composite components, where an additive-manufactured cellular solid is infiltrated by a sheet molding compound (SMC) in a single-step compression molding operation. A single-fiber numerical approach is adopted to predict microstructural changes, such as fiber orientation, fiber-matrix separation, and fiber volume content variations during molding. The accuracy of the numerical predictions is confirmed by physical samples using micro-computed tomography and optical microscopy investigations at both the qualitative and quantitative scales. From optical microscopy observations, there emerged a positive correlation between experimental outcomes and simulation results, accurately capturing fiber swirling, wrinkling, and draping that occurred during molding. At a quantitative scale, a 0.6% mismatch was observed when void volume and unfilled areas were compared, as measured by micro-computed tomography and numerical simulation. [ABSTRACT FROM AUTHOR]

Efforts from the scientific community and the private sector are required to lower the costs of large‐scale production of nanomaterials (NMs) to enhance their commercialization. In this work, the computer‐aided process design of large‐scale NM synthesis procedures is comprehensively reviewed. Moreover, a generalized process flow diagram for all large‐scale production processes was constructed by surveying numerous scalable experimental procedures reported in the literature. Previous studies reporting simulation cases of large‐scale production processes of NMs and nanocomposites (NCs) are also reviewed based on the type of material produced, e.g., oxides, sulfides, carbonaceous materials, organic materials, metals, and other types of NMs. Finally, technical insights from classical chemical engineering specializations, such as altering process configurations, optimizing process variables, integrating chemical processes, utilizing renewable energy sources, conducting computational calculations, employing machine learning techniques, and studying the process's environmental impact, are reviewed for large‐scale NMs and NCs synthesis. [ABSTRACT FROM AUTHOR]

Water hyacinth (WH) is an aquatic weed with an experimentally proven potential as a feedstock for bioethanol production. Unlike other bioethanol feedstocks, water hyacinth has no requirement for land use and resource consumption for cultivation. This study evaluates scaled-up bioethanol production process routes, modelled using the Aspen Plus process simulator to analyse the process performance of water hyacinth as a bioethanol feedstock. Four process scenarios are developed by combining two different feedstock pretreatment methods (i.e., alkali pretreatment and diluted acid pretreatment) and bioethanol dehydration techniques (i.e., azeotropic distillation and extractive distillation). Mass and energy flows of the four scenarios are comparatively analysed. Results show that the alkali pretreatment method provides a higher bioethanol yield (i.e., 254 L/tonne-WH) compared with the dilute acid pretreatment method (i.e., 210 L/tonne-WH). In addition, the process route combining alkali pretreatment and extractive dehydration techniques indicates the least process energy consumption of 45,310 MJ/m3 of bioethanol. The process energy flow analysis evaluates two energy sustainability indicators, i.e., net energy gain and renewability factor, with further interpretation of variation effects of the key process parameters through a sensitivity analysis. The feasible ways of utilising water hyacinth as a fuel-grade bioethanol feedstock for industrial-scale production are discussed. [ABSTRACT FROM AUTHOR]

Bioenergy recovery from biomass is essential to address an eco-friendly disposal route for industrial by-products. This study focuses on the energy recovery from agro-industrial by-products in a combined heat and power unit (CHP) operating with a gas turbine fed with biogas. A rigorous thermodynamic model was presented and used for the energy generation assessment. The biogas was produced from anaerobic digestion (AD) of macaúba and açaí by-products with and without subcritical water pretreatment. The proposed methodology is easily applied and may provide answers to subside the conceptual design of an industrial plant operating with a CHP with a gas turbine for energy recovery. Simulation results showed electric and thermal energy potentials of, respectively, 3.4 and 11.2 MW when pretreatment is applied before the AD of macaúba shells. The thermal energy surplus was 58% higher than that obtained with direct AD of macaúba shells. On the other hand, applying subcritical water pretreatment of açaí seeds followed by AD to obtain biogas was not energetically profitable. However, feeding biogas from the direct AD of açaí seeds into the CHP would produce 3.4 and 7 MW, respectively, of electric and thermal energy. Moreover, considering the electricity replacement of 3.4 MW, 3699 tCO2-eq year−1 of greenhouse gas emissions could be avoided. Finally, using process simulation of first principle models for energy assessment was evinced to be a powerful tool to evaluate the viability of CHP operating with a gas turbine for bioenergy recovery in the food industry. [ABSTRACT FROM AUTHOR]

Natural pigment production represents an innovative and sustainable alternative to synthetic pigments. However, its industrial production to meet the global demand for pigments poses technological and economic challenges. In this work, a process design and simulation were conducted using SuperPro Designer to produce a blue natural pigment known as indigoidine, which is in high demand as a natural alternative to synthetic blue dyes in industries. The process design included upstream, bioreaction, and downstream processing to produce 113 tons per year of dry indigoidine. For the conception and design of the bioprocess, experimental data reported in the literature, such as kinetic and stoichiometric parameters, culture media, feeding strategy, and volumetric power input, were taken into account. The economic and profitability indicators of four scenarios were assessed based on a base scenario, which involved changing the typical stirred tank reactor to an airlift reactor, decreasing indigoidine recovery, and reducing biomass production. It was estimated that the use of an airlift reactor significantly improves the profitability of the bioprocess, while a 50 % decrease in biomass concentration (less than 40 g/L) significantly affected the profitability of the process. Finally, an equilibrium production point of around 56 tons per year was determined to balance total revenues with operational costs. This is the first work that offers valuable insights into the scaling-up of natural pigment indigoidine production using bacteria. [ABSTRACT FROM AUTHOR]

The development of wind power has brought about increasing challenges in decommissioning, among which DWTBs (decommissioned wind turbine blades) are the most difficult component to deal with. To enable the cost-effective, energy-efficient, and environmentally friendly large-scale utilization of DWTBs, an experimental study on thermogravimetric and pyrolysis characteristics of DWTBs was carried out. A new process involving recycling glass fiber with pyrolysis gas re-combustion and flue gas recirculation as the pyrolysis medium was innovatively proposed, and the simulation calculation was carried out. Thermogravimetric experiments indicated that glass fiber reinforced composite (GFRC) was the main heat-generating part in the heat utilization process of blades, and the blade material could basically complete pyrolysis at 600 °C. As the heating rate increased, the formation temperature, peak concentration, and proportion of combustible gas in the pyrolysis gas also increased. The highest peak concentration of CO gas was observed, with CO2 and C3H6 reaching their peaks at 700 °C. The solid product obtained from pyrolysis at 600 °C could be oxidized at 550 °C for 40 min to obtain clean glass fiber. And the pyrolysis temperature increased with the increase in the proportion of recirculation flue gas. When the proportion of recirculation flue gas was 66%, the pyrolysis temperature could reach 600 °C, meeting the necessary pyrolysis temperature for wind turbine blade materials. The above research provided fundamental data support for further exploration on high-value-added recycling of DWTBs. [ABSTRACT FROM AUTHOR]

The hydrogen–water isotope catalytic exchange process has been widely applied in the tritium-containing water treatment process. It can be compared and analyzed conveniently with process simulation software. In this study, the catalytic exchange process was simulated by Aspen Plus software (V11). According to the simulation results, the main reaction process was that HDO in the liquid phase converts into HD in the gas phase, and the reaction mainly occurred at the bottom of the column, exhibiting a two-orders-of-magnitude-higher reaction amount compared to that observed in the top section. Different side reactions occur at distinct positions along the column, exhibiting a reaction amount that is lower by one to two orders of magnitude compared to the main reaction and aligning in the same direction as the main reaction. The optimum operating temperature is 60~80 °C, with the best performance observed at 70 °C, because of the large reaction equilibrium constant and the suitable ratio of vapor to hydrogen (1:4~1:1.5) in the gas phase. The influence of the residence time was investigated by introducing reaction kinetic equations. The residence time should be more than 1 s to ensure an adequate reaction. The influence of operating conditions on the hydrogen–water isotope catalytic exchange process can be deeply investigated by process simulation, and more mass transfer process quantities can be obtained. It plays a promoting role in guiding the process design and condition optimization. [ABSTRACT FROM AUTHOR]

The technical and economic challenges are enormous in developing gas wells with a high CO2 content (> 50 mol %), such as the East Natuna gas field, which has 71 mol % (86 mass %). Carbon capture technology in gas wells with high CO2 content is studied to find a more economical one. In this study, the technology for capturing high concentrations of CO2 is varied from gas wells, Controlled Freeze Zone (CFZ), and cryogenic distillation which will be analyzed based on cost. Variations in the CO2 content of gas wells are 75 %, 80 %, 85 %, 90 %, and 95 % in mass fraction simulated using Aspen Plus V.11.0. Based on this research, CFZ technology and cryogenic distillation can obtain purity up to 99 % and recovery above 78 %. However, the total annual cost (annual capital and operating costs) of CFZ technology is lower than cryogenic distillation technology, so CFZ technology is suitable for capturing CO2 in gas wells with high CO2 content. [ABSTRACT FROM AUTHOR]

As in the transport sector, it is also necessary to investigate alternatives to fossil diesel fuel in the agricultural sector. In addition to technical feasibility, ecological viability and social acceptance, economic viability is ultimately decisive in determining whether an alternative energy source is competitive. Against this background, in this paper, a cost calculation model is described to analyze the competitiveness of different future technologies and applied on the case of future electrical energy supply concepts. The model is applied to compare a continuous energy supply system via a wide span system (WSS) with a discontinuous battery swapping system (BSS) on the example of tillage. The results show that on area sizes of more than 100 ha, a continuous energy supply for agricultural machinery with a WSS is competitive to a discontinuous BSS. [ABSTRACT FROM AUTHOR]

Maximizing the use of digitally captured data is a key requirement for many of the late adopters of digital infrastructure. One of the newcomers is the chemical industry in the area of digitized laboratories. Here, tools and services that satisfy individual needs still need to be developed and distributed within the community. This work explores the potential of using graph databases — specifically those modeled via ontological knowledge graphs — to describe complex data linkages and draw logical conclusions. While knowledge graphs are not widely utilized in catalysis research, this study introduces a methodology to highlight their usability for semantic description and integration into diverse value chains with contact to the domain of (bio)chemistry and catalysis. A demonstration is performed how ontologies and their knowledge graphs can be applied to perform essential functions of semantic annotation to chemical reactions, which are difficult to model relational. Traditional data description methods can be neglected using description logic, showing how logical inferences at the machine level can enrich data. This work also illustrates the seamless integration of this enhanced data into process simulations, connecting semantic description with practical applications. The immediate benefits for catalysis research are emphasized and the development of new tools and services envisioned. By clarifying how these graphs can be integrated into existing workflows, researchers are empowered to make the most of digitally acquired data in catalytic processes. This practical methodology lays the foundation for improved decision-making and innovation, fostering advancements in the field of catalysis research. [ABSTRACT FROM AUTHOR]